The generalization of deepfake detectors to unseen manipulation techniques remains a challenge for practical deployment. Although many approaches adapt foundation models by introducing significant architectural complexity, this work demonstrates that robust generalization is achievable through a parameter-efficient adaptation of one of the foundational pre-trained vision encoders. The proposed method, GenD, fine-tunes only the Layer Normalization parameters (0.03% of the total) and enhances generalization by enforcing a hyperspherical feature manifold using L2 normalization and metric learning on it. We conducted an extensive evaluation on 14 benchmark datasets spanning from 2019 to 2025. The proposed method achieves state-of-the-art performance, outperforming more complex, recent approaches in average cross-dataset AUROC. Our analysis yields two primary findings for the field: 1) training on paired real-fake data from the same source video is essential for mitigating shortcut learning and improving generalization, and 2) detection difficulty on academic datasets has not strictly increased over time, with models trained on older, diverse datasets showing strong generalization capabilities. This work delivers a computationally efficient and reproducible method, proving that state-of-the-art generalization is attainable by making targeted, minimal changes to a pre-trained foundational image encoder model. The code is at: https://github.com/yermandy/GenD

A novel approach to detect road surface anomalies by visual tracking of a preceding vehicle is proposed. The method is versatile, predicting any kind of road anomalies, such as potholes, bumps, debris, etc., unlike direct observation methods that rely on training visual detectors of those cases. The method operates in low visibility conditions or in dense traffic where the anomaly is occluded by a preceding vehicle. Anomalies are detected predictively, i.e., before a vehicle en-counters them, which allows to pre-configure low-level vehicle systems (such as chassis) or to plan an avoidance maneuver in case of autonomous driving. A challenge is that the signal coming from camera-based tracking of a preceding vehicle may be weak and disturbed by camera ego motion due to vibrations affecting the ego vehicle. Therefore, we propose an efficient method to compensate camera pitch rotation by an iterative robust estimator. Our experiments on both controlled setup and normal traffic conditions show that road anomalies can be detected reliably at a distance even in challenging cases where the ego vehicle traverses imperfect road surfaces. The method is effective and performs in real time on standard consumer hardware.

We present ChunkyGAN—a novel paradigm for modeling and editing images using generative adversarial networks. Unlike previous techniques seeking a global latent representation of the input image, our approach subdivides the input image into a set of smaller components (chunks) specified either manually or automatically using a pre-trained segmentation network. For each chunk, the latent code of a generative network is estimated locally with greater accuracy thanks to a smaller number of constraints. Moreover, during the optimization of latent codes, segmentation can further be refined to improve matching quality. This process enables high-quality projection of the original image with spatial disentanglement that previous methods would find challenging to achieve. To demonstrate the advantage of our approach, we evaluated it quantitatively and also qualitatively in various image editing scenarios that benefit from the higher reconstruction quality and local nature of the approach. Our method is flexible enough to manipulate even out-of-domain images that would be hard to reconstruct using global techniques.

We propose a neural network which takes two inputs, a hair image and a face image, and produces an output image having the hair of the hair image seamlessly merged with the inner face of the face image. Our architecture consists of neural networks mapping the input images into a latent code of a pretrained StyleGAN2 which generates the output high-definition image. We propose an algorithm for training parameters of the architecture solely from synthetic images generated by the StyleGAN2 itself without the need of any annotations or external dataset of hairstyle images. We empirically demonstrate the effectiveness of our method in applications including hair-style transfer, hair generation for 3D morphable models, and hair-style interpolation. Fidelity of the generated images is verified by a user study and by a novel hairstyle metric proposed in the paper.

A self-supervised method to train a visual predictor of drivable surface roughness in front of a vehicle is proposed. A convolutional neural network taking a single camera image is trained on a dataset labeled automatically by a cross-modal supervision. The dataset is collected by driving a vehicle on various surfaces, while synchronously recording images and accelerometer data. The surface images are labeled by the local roughness measured using the accelerometer signal aligned in time. Our experiments show that the proposed training scheme results in accurate visual predictor. The correlation coefficient between the visually predicted roughness and the true roughness (measured by the accelerometer) is 0.9 on our independent test set of about 1000 images. The proposed method clearly outperforms a baseline method which has the correlation of 0.3 only. The baseline is based on surface texture strength without any training. Moreover, we show a coarse map of local surface roughness, which is implemented by scanning an input image with the trained convolutional network. The proposed method provides automatic and objective road condition assessment, enabling a cheap and reliable alternative to manual data annotation, which is infeasible in a large scale.

We propose a CNN-based approach to classify ten genres of ballroom dances given audio recordings, five latin and five standard, namely Cha Cha Cha, Jive, Paso Doble, Rumba, Samba, Quickstep, Slow Foxtrot, Slow Waltz, Tango and Viennese Waltz. We utilize a spectrogram of an audio signal and we treat it as an image that is an input of the CNN. The classification is performed independently by 5-seconds spectrogram segments in sliding window fashion and the results are then aggregated. The method was tested on following datasets: Publicly available Extended Ballroom dataset collected by Marchand and Peeters, 2016 and two YouTube datasets collected by us, one in studio quality and the other, more challenging, recorded on mobile phones. The method achieved accuracy 93.9%, 96.7% and 89.8% respectively. The method runs in real-time. We implemented a web application to demonstrate the proposed method.

The concept of a visually-assisted anti-lock braking system (ABS) is presented. The road conditions in front of the vehicle are assessed in real time based on camera data. The surface type classification (tarmac, gravel, ice, etc.) and related road friction properties are provided to the braking control algorithm in order to adjust the vehicle response accordingly. The system was implemented and tested in simulations as well as on an instrumented sub-scale vehicle. Simulations and experimental results quantitatively demonstrate the benefits of the proposed system in critical maneuvers, such as emergency braking and collision avoidance.

In situations when potentially costly decisions are being made, faces of people tend to reflect a level of certainty about the appropriateness of the chosen decision. This fact is known from the psychological literature. In the paper, we propose a method that uses facial images for automatic detection of the decision ambiguity state of a subject. To train and test the method, we collected a large-scale dataset from "Who Wants to Be a Millionaire?" -- a popular TV game show. The videos provide examples of various mental states of contestants, including uncertainty, doubts and hesitation. The annotation of the videos is done automatically from on-screen graphics. The problem of detecting decision ambiguity is formulated as binary classification. Video-clips where a contestant asks for help (audience, friend, 50:50) are considered as positive samples; if he (she) replies directly as negative ones. We propose a baseline method combining a deep convolutional neural network with an SVM. The method has an error rate of 24%. The error of human volunteers on the same dataset is 45%, close to chance.

Photoplethysmography (PPG) is a non-invasive method of measuring changes of blood volume in human tissue. The literature on non-contact reflectance PPG related to cardiovascular activity is extensively reviewed. We identify key factors limiting the performance of the PPG methods and reproducibility of the research as: a lack of publicly available datasets and incomplete description of data used in published experiments (missing details on video compression, lighting setup and subject's skin type), use of unreliable pulse oximeter devices for ground-truth reference and missing standard experimental protocols. Two experiments with 5 participants are presented showing that the quality of the reconstructed signal (1) is adversely affected by a reduction of spatial resolution that also amplifies the effects of H.264 video compression and (2) is improved by precise pixel-to-pixel stabilization.

Supervised learning of convolutional neural networks (CNNs) for face recognition requires a large set of facial images each annotated with a single attribute label to be predicted. In this paper we propose a method for learning CNNs from weakly annotated images. The weak annotation in our setting means that a pair of an attribute label and a person identity label is assigned to a set of faces automatically detected in the image. The challenge is to link the annotation with the correct face. The weakly annotated images of this type can be collected by an automated process not requiring a human labor. We formulate learning from weakly annotated images as a maximum likelihood estimation of a parametric distribution describing the data. The ML problem is solved by an instance of EM algorithm which in its inner loop learns a CNN to perform given face recognition task. Experiments on age and gender estimation problem show that the proposed EM-CNN algorithm significantly outperforms the state-of-theart approach for dealing with this type of data.

In this paper, we survey different state-of-the-art visual processing methods and utilize them in hyperlinking. Visual information, calculated using Features Signatures, SIMILE descriptors and convolutional neural networks (CNN), is utilized as similarity between video frames and used to find similar faces, objects and setting. Visual concepts in frames are also automatically recognized and textual output of the recognition is combined with search based on subtitles and transcripts. All presented experiments were performed in the Search and Hyperlinking 2014 MediaEval task and Video Hyperlinking 2015 TRECVid task.

A real-time algorithm for accurate localization of facial landmarks in a single monocular image is proposed. The algorithm is formulated as an optimization problem, in which the sum of responses of local classifiers is maximized with respect to the camera pose by fitting a generic (not a person-specific) 3D model. The algorithm simultaneously estimates a head position and orientation and detects the facial landmarks in the image. Despite being local, we show that the basin of attraction is large to the extent it can be initialized by a scanning window face detector. Other experiments on standard datasets demonstrate that the proposed algorithm outperforms a state-of-the-art landmark detector especially for non-frontal face images, and that it is capable of reliable and stable tracking for large set of viewing angles.

The combination of range sensors with color cameras can be very useful for robot navigation, semantic perception, manipulation, and telepresence. Several methods of combining range- and color-data have been investigated and successfully used in various robotic applications. Most of these systems suffer from the problems of noise in the range-data and resolution mismatch between the range sensor and the color cameras, since the resolution of current range sensors is much less than the resolution of color cameras. High-resolution depth maps can be obtained using stereo matching, but this often fails to construct accurate depth maps of weakly/repetitively textured scenes, or if the scene exhibits complex self-occlusions. Range sensors provide coarse depth information regardless of presence/absence of texture. The use of a calibrated system, composed of a time-of-flight (TOF) camera and of a stereoscopic camera pair, allows data fusion thus overcoming the weaknesses of both individual sensors. We propose a novel TOF-stereo fusion method based on an efficient seed-growing algorithm which uses the TOF data projected onto the stereo image pair as an initial set of correspondences. These initial “seeds” are then propagated based on a Bayesian model which combines an image similarity score with rough depth priors computed from the low-resolution range data. The overall result is a dense and accurate depth map at the resolution of the color cameras at hand. We show that the proposed algorithm outperforms 2D image-based stereo algorithms and that the results are of higher resolution than off-the-shelf color-range sensors, e.g., Kinect. Moreover, the algorithm potentially exhibits real-time performance on a single CPU.

A simple seed growing algorithm for estimating scene flow in a stereo setup is presented. Two calibrated and synchronized cameras observe a scene and output a sequence of image pairs. The algorithm simultaneously computes a disparity map between the image pairs and optical flow maps between consecutive images. This, together with calibration data, is an equivalent representation of the 3D scene flow, i.e. a 3D velocity vector is associated with each reconstructed point. The proposed method starts from correspondence seeds and propagates these correspondences to their neighborhood. It is accurate for complex scenes with large motions and produces temporally-coherent stereo disparity and optical flow results. The algorithm is fast due to inherent search space reduction. An explicit comparison with recent methods of spatiotemporal stereo and variational optical and scene flow is provided.

3D Shape matching is an important problem in computer vision. One of the major difficulties in finding dense correspondences between 3D shapes is related to the topological discrepancies that often arise due to complex kinematic motions. In this paper we propose a shape matching method that is robust to such changes in topology. The algorithm starts from a sparse set of seed matches and outputs dense matching. We propose to use a shape descriptor based on properties of the heat-kernel and which provides an intrinsic scale-space representation. This descriptor incorporates (i) heat-flow from already matched points and (ii) self diffusion. At small scales the descriptor behaves locally and hence it is robust to global changes in topology. Therefore, it can be used to build a vertex-to-vertex matching score conditioned by an initial correspondence set. This score is then used to iteratively add new correspondences based on a novel seed-growing method that iteratively propagates the seed correspondences to nearby vertices. The matching is farther densified via an EM-like method that explores the congruency between the two shape embeddings. Our method is compared with two recently proposed algorithms and we show that we can deal with substantial topological differences between the two shapes.

The scene flow in binocular stereo setup is estimated using a seed growing algorithm. A pair of calibrated and synchronized cameras observe a scene and output a sequence of images. The algorithm jointly computes a disparity map between the stereo images and optical flow maps between consecutive frames. Having the calibration, this is a representation of the scene flow, i.e. a 3D velocity vector is associated with each reconstructed 3D point. The proposed algorithm starts from correspondence seeds and propagates the correspondences to the neighborhood. It is accurate for complex scenes with large motion and produces temporally coherent stereo disparity and optical flow results. The algorithm is fast due to inherent search space reduction.

In many retrieval, object recognition, and wide-baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as SIFTs. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that 1) has high precision (is highly discriminative), 2) has good recall, and 3) is fast. The sequential decision on the correctness of a correspondence is based on simple statistics of a modified dense stereo matching algorithm. The statistics are projected on a prominent discriminative direction by SVM. Wald's sequential probability ratio test is performed on the SVM projection computed on progressively larger cosegmented regions. We show experimentally that the proposed sequential correspondence verification (SCV) algorithm significantly outperforms the standard correspondence selection method based on SIFT distance ratios on challenging matching problems.

In many retrieval, object recognition and wide baseline stereo methods, correspondences of interest points are established possibly sublinearly by matching a compact descriptor such as SIFT. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that has (i) high precision (is highly discriminative) (ii) good recall and (iii) is fast. The sequential decision on the correctness of a correspondence is based on trivial attributes of a modified dense stereo matching algorithm. The attributes are projected on a prominent discriminative direction by SVM. Waldpsilas sequential probability ratio test is performed for SVM projection computed on progressively larger co-segmented regions. Experimentally we show that the process significantly outperforms the standard correspondence selection process based on SIFT distance ratios on challenging matching problems.

A simple stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. Unlike in known seed-growing algorithms, it guarantees matching accuracy and correctness, even in the presence of repetitive patterns. This success is based on the fact it solves a global optimization task. The algorithm can recover from wrong initial seeds to the extent they can even be random. The quality of correspondence seeds influences computing time, not the quality of the final disparity map. We show that the proposed algorithm achieves similar results as an exhaustive disparity space search but it is two orders of magnitude faster. This is very unlike the existing growing algorithms which are fast but erroneous. Accurate matching on 2-megapixel images of complex scenes is routinely obtained in a few seconds on a common PC from a small number of seeds, without limiting the disparity search range.

In stereo literature, there is no standard method for evaluating algorithms for semi-dense stereo matching. Moreover, existing evaluations for dense methods require a fixed parameter setting for the tested algorithms. In this paper, we propose a method that overcomes these drawbacks and still is able to compare algorithms based on a simple numerical value, so that reporting results does not take up much space in a paper. We propose evaluation of stereo algorithms based on Receiver Operating Characteristics (ROC) which captures both errors and sparsity. By comparing ROC curves of all tested algorithms we obtain the Feasibility Boundary, the best possible performance achieved by a set of tested stereo algorithms, which allows stereo algorithm users to select the proper method and parameter setting for a required application.

In this paper we propose and study a simple trinocular rectification method in which stratification to projective and affine components gives the rectifying homographies in a closed form. The class of trinocular rectifications which has 6 DOF is parametrized by an independent set of parameters with a geometric meaning. This offers the possibility to minimize rectification distortion in a natural way. It is shown experimentally on real data that our algorithm performs the rectification task correctly. As shown on groundtruth data using Confidently Stable Matching, trinocular matching significantly improves disparity map density and mismatch error, both depending on texture strength. Matching results on real complex scenes are reported. 1